system

Abstract

The system according to this embodiment aims to collect and analyze environmental data in real time, determine the risk of environmental pollution, and implement early warnings and preventive measures. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a judgment unit, and an implementation unit. The collection unit collects environmental data. The analysis unit analyzes the data collected by the collection unit. The judgment unit determines the risk of environmental pollution based on the analysis results obtained by the analysis unit. The implementation unit implements early warnings and preventive measures based on the risk determined by the judgment unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the collection and analysis of environmental data are not performed in real time, and it is difficult to detect and prevent environmental pollution at an early stage.

[0005] The system according to the embodiment aims to collect and analyze environmental data in real time, judge the risk of environmental pollution, and implement early warnings and preventive measures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a decision unit, and an implementation unit. The data collection unit collects environmental data. The analysis unit analyzes the data collected by the data collection unit. The decision unit determines the risk of environmental pollution based on the analysis results obtained by the analysis unit. The implementation unit implements early warnings and preventive measures based on the risk determined by the decision unit. [Effects of the Invention]

[0007] The system according to this embodiment can collect and analyze environmental data in real time, determine the risk of environmental pollution, and implement early warnings and preventive measures. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The environmental monitoring system according to an embodiment of the present invention is a system that comprehensively supports environmental protection and education by utilizing AI and IoT. This system provides three main functions: real-time collection and analysis of environmental data, support for wildlife conservation activities, and promotion of environmental education and conservation activities. Real-time collection and analysis of environmental data involves collecting environmental data such as air pollution and water pollution in real time using IoT devices. This enables early detection and prevention of environmental pollution. The collected data is analyzed by AI to determine the risk of environmental pollution. For example, the AI ​​processes and analyzes data collected from sensor devices in real time. Furthermore, it utilizes generative AI technologies such as natural language processing and image recognition to extract complex data patterns and trends and evaluate the state of the environment. This enables early warnings and the implementation of preventive measures, minimizing the damage caused by environmental pollution. Next is support for wildlife conservation activities. It supports conservation activities by tracking and monitoring the movements of wild animals using surveillance cameras and drones. For example, video and image data collected from sensor devices such as surveillance cameras and drones are analyzed using generative AI to track the movements and behavioral patterns of wild animals. By utilizing generative AI for pattern recognition and anomaly detection, we can identify key indicators and trends in environmental protection activities and respond immediately. This enables the protection of wildlife habitats and the prevention of illegal hunting. By using AI to analyze animal behavior, we can improve the efficiency of conservation activities. This enhances the effectiveness of conservation efforts and strengthens wildlife protection. Furthermore, we promote environmental education and conservation activities. We use AI to provide environmental education programs and raise environmental awareness. For example, we use generative AI to analyze participants' interests and learning styles and provide environmental education programs tailored to each individual. By tracking users' learning processes and results and providing individualized feedback and improvement measures, we achieve effective environmental education. This helps many people understand the importance of environmental protection and participate in environmental protection activities. We offer incentives and rewards to encourage participation in environmental protection activities. This increases participation in environmental protection activities and improves the effectiveness of environmental protection.This allows the environmental monitoring system to comprehensively collect and analyze environmental data, assess risks, implement preventive measures, support wildlife conservation activities, and provide environmental education.

[0029] The environmental monitoring system according to this embodiment comprises a collection unit, an analysis unit, a decision unit, and an implementation unit. The collection unit collects environmental data. Environmental data includes, but is not limited to, temperature, humidity, air quality, and soil data. The collection unit collects environmental data such as air pollution and water pollution in real time, for example, using IoT devices. The collection unit can also collect environmental data using sensor devices. For example, the collection unit can collect temperature data using a temperature sensor. It can also collect humidity data using a humidity sensor. It can also collect air quality data using an air quality sensor. The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit can analyze trends in environmental data using statistical analysis. It can also analyze environmental data using machine learning algorithms. Furthermore, the analysis unit can also analyze environmental data using generative AI. For example, the generative AI analyzes environmental data using natural language processing or image recognition technology. The decision unit determines the risk of environmental pollution based on the analysis results obtained by the analysis unit. Risk assessment is performed based on, for example, risk thresholds or evaluation criteria. For example, the assessment unit sets risk thresholds and determines that the risk is high if those thresholds are exceeded. Risk can also be assessed based on evaluation criteria. The implementation unit implements early warnings and preventive measures based on the risk assessed by the assessment unit. Early warnings and preventive measures are implemented based on, for example, the type of warning or the specific means of the preventive measure. For example, the implementation unit provides an early warning by issuing an alarm. As a preventive measure, the frequency of collecting environmental data in a specific area can also be increased. This allows the environmental monitoring system to perform environmental data collection, analysis, risk assessment, and preventive measure implementation in a continuous flow. Some or all of the above processes in the collection unit, analysis unit, assessment unit, and implementation unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input data collected from sensor devices into a generating AI, which can then analyze the data.The analysis unit can analyze environmental data using generative AI and make risk assessments. The decision-making unit makes risk assessments using generative AI, and the implementation unit can implement early warnings and preventive measures using generative AI.

[0030] The data collection unit collects environmental data. This environmental data includes, but is not limited to, temperature, humidity, air quality, and soil data. For example, the data collection unit uses IoT devices to collect environmental data such as air and water pollution in real time. Specifically, IoT devices incorporate various sensors that continuously monitor the surrounding environmental data. For instance, a temperature sensor periodically measures the ambient temperature and transmits the data to the data collection unit. A humidity sensor measures the humidity in the air and similarly transmits the data. An air quality sensor detects airborne pollutants such as PM2.5 and CO2 concentration and collects the data. A soil sensor measures soil moisture and nutrient content and provides data. These sensors can transmit data to a central database using wireless communication technology, enabling real-time collection of environmental data. Furthermore, the data collection unit can also mount sensors on mobile devices such as drones and autonomous vehicles to efficiently collect environmental data over a wide area. This allows the data collection unit to collect a wide range of environmental data in real time using diverse devices and means, forming the foundation of an environmental monitoring system.

[0031] The analysis department analyzes the data collected by the collection department. Analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. Specifically, statistical analysis is used to analyze trends in environmental data and detect outliers and patterns. For example, time-series analysis of temperature data can be performed to detect abnormal temperature increases or decreases. When analyzing environmental data using machine learning algorithms, past data is used to build models that predict future environmental changes. For example, temperature, humidity, and air quality data can be used to predict the risk of air pollution in a specific area. Furthermore, the analysis department can also analyze environmental data using generative AI. Generative AI analyzes environmental data using natural language processing and image recognition technologies. For example, generative AI analyzes image data acquired from sensors to detect specific pollution sources or abnormal environmental changes. Generative AI can also automatically generate reports and documents on environmental data using natural language processing technology. This allows the analysis department to analyze the collected data from multiple perspectives and accurately understand the current state of the environment and future risks.

[0032] The decision unit determines the risk of environmental pollution based on the analysis results obtained by the analysis unit. Risk assessment is performed based on, for example, risk thresholds and evaluation criteria, but is not limited to these examples. Specifically, the decision unit sets risk thresholds and determines that the risk is high if those thresholds are exceeded. For example, in air quality data, if the PM2.5 concentration exceeds a certain threshold, it determines that the risk of air pollution is high. Risk can also be determined based on evaluation criteria. For example, temperature, humidity, and air quality data are comprehensively evaluated to make a comprehensive judgment on the environmental risk in a specific area. Furthermore, the decision unit can also perform risk assessment using generative AI. Generative AI learns risk patterns and trends based on past data and statistical information and predicts future risks. For example, generative AI can learn past air pollution data and predict the risk of air pollution under specific weather conditions or seasons. As a result, the decision unit can accurately and quickly determine the risk based on the analysis results and provide information for taking appropriate countermeasures.

[0033] The implementation department implements early warnings and preventive measures based on the risks determined by the judgment department. Early warnings and preventive measures are implemented based on, for example, the type of warning and the specific means of preventive measures, but are not limited to such examples. Specifically, the implementation department provides early warnings by issuing warnings. For example, if the risk of air pollution is determined to be high, the implementation department issues a warning to local residents and notifies them to refrain from going outside. In addition, as a preventive measure, the frequency of collecting environmental data in a particular area can be increased. For example, if the risk of water pollution is determined to be high, the implementation department increases the frequency of collecting water quality data in that area and collects more detailed data. Furthermore, the implementation department can also implement early warnings and preventive measures using generative AI. Generative AI proposes optimal warnings and preventive measures based on data that is updated in real time. For example, generative AI can analyze weather data and environmental data and propose optimal evacuation routes and evacuation locations when the risk in a particular area increases. This allows the implementation department to take swift and appropriate measures based on the determined risks and minimize environmental risks.

[0034] The environmental monitoring system includes a monitoring unit that tracks and monitors the movements of wild animals using surveillance cameras and drones. The monitoring unit, for example, monitors the movements of wild animals using surveillance cameras. Surveillance cameras, for example, can monitor the movements of wild animals in detail using high-resolution cameras. The monitoring unit can also track the movements of wild animals using drones. Drones, for example, can monitor the movements of wild animals while flying over a wide area. Furthermore, the monitoring unit can analyze the monitoring data using generative AI. For example, the monitoring unit inputs video and image data collected from surveillance cameras and drones into the generative AI, which then analyzes the data. This allows the monitoring unit to accurately track the movements and behavioral patterns of wild animals. Some or all of the above-described processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input data collected from surveillance cameras and drones into the generative AI, which then analyzes the data. This allows for monitoring the movements of wild animals and supporting conservation efforts.

[0035] The environmental monitoring system includes an education delivery unit that provides environmental education programs using AI. The education delivery unit, for example, uses generative AI to analyze participants' interests and learning styles and provides individually tailored environmental education programs. For instance, the education delivery unit can analyze participants' learning data using generative AI and provide individually optimized education programs. Furthermore, the education delivery unit can track users' learning processes and outcomes and provide individualized feedback and improvement measures. For example, the education delivery unit can analyze users' learning data using generative AI and provide individualized feedback. This enables the education delivery unit to achieve effective environmental education. Some or all of the above-described processes in the education delivery unit may be performed using AI, for example, or without AI. For example, the education delivery unit can analyze participants' learning data using generative AI and provide individually optimized education programs. This enables the provision of environmental education programs and improves environmental awareness.

[0036] The environmental monitoring system includes a rewards provision unit that offers incentives and rewards to encourage participation in environmental protection activities. The rewards provision unit can, for example, encourage participation in environmental protection activities using a point system. For instance, the unit can award points for participation in environmental protection activities, which can then be exchanged for incentives and rewards. Furthermore, the rewards provision unit can also provide monetary rewards. For example, the unit can offer monetary rewards for participation in environmental protection activities. This allows the rewards provision unit to encourage many people to participate in environmental protection activities. Some or all of the above-described processes in the rewards provision unit may be performed using, for example, AI, or not. For example, the rewards provision unit can use generative AI to analyze participant activity data and provide optimal incentives and rewards. This can promote participation in environmental protection activities and improve the effectiveness of environmental protection.

[0037] The monitoring unit can analyze video and image data using generative AI to track the movements and behavioral patterns of wild animals. For example, the monitoring unit can analyze video and image data collected from surveillance cameras and drones using generative AI. For example, the monitoring unit can analyze and track the movements and behavioral patterns of wild animals using generative AI. Furthermore, the monitoring unit can also perform anomaly detection using generative AI. For example, the monitoring unit can detect abnormal behavioral patterns using generative AI and respond immediately. This allows the monitoring unit to accurately track the movements and behavioral patterns of wild animals. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can analyze data collected from surveillance cameras and drones using generative AI to track the movements and behavioral patterns of wild animals. This allows for accurate tracking of the movements and behavioral patterns of wild animals by using generative AI.

[0038] The monitoring unit can utilize generative AI for pattern recognition and anomaly detection to grasp important indicators and trends in environmental protection activities and respond immediately. For example, the monitoring unit can analyze monitoring data using generative AI to grasp important indicators and trends. For example, the monitoring unit can detect abnormal behavioral patterns using generative AI and respond immediately. Furthermore, the monitoring unit can also grasp important indicators in environmental protection activities using generative AI. For example, the monitoring unit can analyze important indicators in environmental protection activities using generative AI and respond immediately. This allows the monitoring unit to quickly grasp and respond to important indicators and trends in environmental protection activities. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can analyze monitoring data using generative AI to grasp important indicators and trends. This allows the monitoring unit to quickly grasp and respond to important indicators and trends in environmental protection activities by using generative AI.

[0039] The education department can use generative AI to analyze participants' interests and learning styles and provide individually tailored environmental education programs. For example, the education department can use generative AI to analyze participants' learning data and provide individually optimized educational programs. Furthermore, the education department can use generative AI to analyze participants' learning data and provide individualized feedback. This allows the education department to provide individually tailored environmental education programs. Some or all of the above processing in the education department may be performed using AI, or not. For example, the education department can use generative AI to analyze participants' learning data and provide individually optimized educational programs. This allows the education department to provide individually tailored environmental education programs by using generative AI.

[0040] The education delivery department can track users' learning processes and outcomes and provide individualized feedback and improvement measures. For example, the education delivery department can analyze users' learning data using generative AI and provide individualized feedback. Furthermore, the education delivery department can track users' learning processes using generative AI and provide individualized improvement measures. This enables the education delivery department to achieve effective environmental education. Some or all of the above-described processes in the education delivery department may be performed using AI, for example, or without AI. For example, the education delivery department can analyze users' learning data using generative AI and provide individualized feedback and improvement measures. This enables effective environmental education by tracking users' learning processes and outcomes and providing individualized feedback and improvement measures.

[0041] The data collection unit can change its collection method based on specific environmental conditions or events when collecting environmental data. For example, if air pollution is high, the data collection unit can increase the collection frequency to collect more detailed data. For example, if water pollution occurs, the data collection unit can use specific sensors to collect data. Furthermore, the data collection unit can also change its collection method to collect data when an environmental event occurs. For example, the data collection unit can change its collection method based on specific environmental conditions or events to collect more detailed data. This allows the data collection unit to collect more detailed and relevant data by changing its collection method based on specific environmental conditions or events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use generative AI to analyze environmental conditions or events and change its collection method. This allows the data collection to collect more detailed and relevant data by changing the collection method based on specific environmental conditions or events.

[0042] The data collection unit can collect environmental data from multiple perspectives by combining different sensor devices. For example, when collecting air pollution data, the data collection unit can use multiple sensors to collect detailed data. For example, when collecting water pollution data, the data collection unit can use different sensors to collect data. Furthermore, when collecting environmental data, the data collection unit can use multiple sensors to collect data from multiple perspectives. For example, the data collection unit can collect data from multiple perspectives by combining different sensor devices. This enables the data collection unit to collect data from multiple perspectives by combining different sensor devices. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use generative AI to analyze data from different sensor devices and collect data from multiple perspectives. This enables the data collection from multiple perspectives by combining different sensor devices.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information when collecting environmental data. For example, the data collection unit can prioritize the collection of data from areas with high air pollution. For example, the data collection unit can prioritize the collection of data from areas where water pollution is occurring. Furthermore, the data collection unit can also prioritize the collection of data from areas where environmental events are occurring. For example, the data collection unit can prioritize the collection of highly relevant data by considering geographical location information. In this way, the data collection unit can prioritize the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can analyze geographical location information using generative AI and prioritize the collection of highly relevant data. In this way, the data collection unit can prioritize the collection of highly relevant data by considering geographical location information.

[0044] The data collection unit can analyze social media activity and collect relevant data when collecting environmental data. For example, the data collection unit can collect data related to environmental issues that are trending on social media. For example, the data collection unit can collect data related to environmental events reported on social media. Furthermore, the data collection unit can also collect environmental data shared on social media. For example, the data collection unit can analyze social media activity and collect relevant data. In this way, the data collection unit can collect relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze social media activity using generative AI and collect relevant data. In this way, relevant data can be collected by analyzing social media activity.

[0045] The analysis unit can apply algorithms to detect anomalies by comparing them with past data during analysis. For example, the analysis unit can detect anomalies by comparing them with past air pollution data. For example, the analysis unit can detect anomalies by comparing them with past water pollution data. Furthermore, the analysis unit can also detect anomalies by comparing them with past environmental data. For example, the analysis unit can apply algorithms to detect anomalies by comparing them with past data to detect abnormal environmental changes at an early stage. In this way, the analysis unit can detect abnormal environmental changes at an early stage by detecting anomalies by comparing them with past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can use generative AI to compare with past data and detect anomalies. In this way, abnormal environmental changes can be detected at an early stage by detecting anomalies by comparing them with past data.

[0046] The analysis unit can integrate different data sources to perform a comprehensive analysis during the analysis process. For example, the analysis unit can integrate air pollution data and water pollution data for a comprehensive analysis. For example, the analysis unit can integrate environmental data and social media data for a comprehensive analysis. Furthermore, the analysis unit can integrate data from different sensor devices for a comprehensive analysis. For example, the analysis unit can integrate different data sources for a comprehensive analysis. This enables the analysis unit to perform a comprehensive analysis by integrating different data sources. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to integrate different data sources and perform a comprehensive analysis. This enables a comprehensive analysis by integrating different data sources.

[0047] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. For example, the analysis unit may prioritize the analysis of data collected during a specific period. Furthermore, the analysis unit may prioritize the analysis of data from the time an environmental event occurred. For example, the analysis unit can determine the priority of analysis based on the data collection timing and prioritize the analysis of the most recent data. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may use generative AI to analyze the data collection timing and determine the priority of analysis. This allows the analysis to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing.

[0048] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and research data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research data on environmental pollution. For example, the analysis unit can improve the accuracy of its analysis by referring to literature on air pollution. Furthermore, the analysis unit can also improve the accuracy of its analysis by referring to literature on water pollution. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature and research data. In this way, the analysis unit improves the accuracy of its analysis by referring to relevant literature and research data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can improve the accuracy of its analysis by analyzing relevant literature and research data using generative AI. In this way, the accuracy of its analysis improves by referring to relevant literature and research data.

[0049] The decision-making unit can predict the current risk by referring to past risk data when making a decision. For example, the decision-making unit can predict the current risk by referring to past air pollution risk data. For example, the decision-making unit can predict the current risk by referring to past water pollution risk data. Furthermore, the decision-making unit can also predict the current risk by referring to past environmental risk data. For example, the decision-making unit can predict the current risk by referring to past risk data. In this way, the decision-making unit can accurately predict the current risk by referring to past risk data. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can analyze past risk data using generative AI and predict the current risk. In this way, the decision-making unit can accurately predict the current risk by referring to past risk data.

[0050] The decision-making unit can perform a comprehensive risk assessment by combining different risk factors at the time of decision-making. For example, the decision-making unit can perform a comprehensive risk assessment by combining air pollution risk and water pollution risk. For example, the decision-making unit can perform a comprehensive risk assessment by combining environmental risk and health risk. Furthermore, the decision-making unit can also perform a comprehensive risk assessment by combining different risk factors. For example, the decision-making unit can perform a comprehensive risk assessment by combining different risk factors. This makes it possible for the decision-making unit to perform a comprehensive risk assessment by combining different risk factors. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can analyze different risk factors using generative AI and perform a comprehensive risk assessment. This makes it possible for the decision-making unit to perform a comprehensive risk assessment by combining different risk factors.

[0051] The decision-making unit can perform risk assessments while considering geographical location information. For example, the decision-making unit can prioritize the assessment of risks in areas with high air pollution. For example, the decision-making unit can prioritize the assessment of risks in areas where water pollution is occurring. Furthermore, the decision-making unit can also prioritize the assessment of risks in areas where environmental events are occurring. For example, the decision-making unit can perform risk assessments while considering geographical location information. This enables the decision-making unit to perform risk assessments for each region by considering geographical location information. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can analyze geographical location information using generative AI and perform risk assessments. This enables risk assessments for each region by considering geographical location information.

[0052] The decision-making unit can assess risk by referring to relevant market data when making a decision. For example, the decision-making unit can assess risk by referring to market data on environmental pollution. For example, the decision-making unit can assess risk by referring to market data on air pollution. Furthermore, the decision-making unit can also assess risk by referring to market data on water pollution. For example, the decision-making unit can assess risk by referring to relevant market data. This improves the accuracy of risk assessment by referring to relevant market data. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can analyze relevant market data using generative AI and assess risk. This improves the accuracy of risk assessment by referring to relevant market data.

[0053] The implementation unit can select the optimal preventive measures by referring to past implementation data during implementation. For example, the implementation unit can select the optimal preventive measures by referring to past air pollution prevention data. For example, the implementation unit can select the optimal preventive measures by referring to past water pollution prevention data. Furthermore, the implementation unit can also select the optimal preventive measures by referring to past environmental prevention data. For example, the implementation unit can select the optimal preventive measures by referring to past implementation data. In this way, the implementation unit can select the optimal preventive measures by referring to past implementation data. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without using AI. For example, the implementation unit can analyze past implementation data using generative AI and select the optimal preventive measures. In this way, the optimal preventive measures can be selected by referring to past implementation data.

[0054] The implementation unit can combine different preventive measures to implement comprehensive measures during implementation. For example, the implementation unit can combine air pollution prevention measures and water pollution prevention measures to implement comprehensive measures. For example, the implementation unit can combine environmental prevention measures and health prevention measures to implement comprehensive measures. Furthermore, the implementation unit can also combine different preventive measures to implement comprehensive measures. For example, the implementation unit can combine different preventive measures to implement comprehensive measures. This makes it possible for the implementation unit to implement comprehensive measures by combining different preventive measures. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can use generative AI to analyze different preventive measures and implement comprehensive measures. This makes it possible to implement comprehensive measures by combining different preventive measures.

[0055] The implementation unit can select the most appropriate preventive measures at the time of implementation, taking geographical location information into consideration. For example, the implementation unit can prioritize the implementation of preventive measures in areas with high air pollution. For example, the implementation unit can prioritize the implementation of preventive measures in areas where water pollution is occurring. Furthermore, the implementation unit can also prioritize the implementation of preventive measures in areas where environmental events are occurring. For example, the implementation unit can select the most appropriate preventive measures by taking geographical location information into consideration. This allows the implementation unit to select the most appropriate preventive measures for each region by taking geographical location information into consideration. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without using AI. For example, the implementation unit can analyze geographical location information using generative AI and select the most appropriate preventive measures. This allows the implementation unit to select the most appropriate preventive measures for each region by taking geographical location information into consideration.

[0056] The implementing unit can improve the accuracy of preventive measures by referring to relevant literature and research data during implementation. For example, the implementing unit can improve the accuracy of preventive measures by referring to the latest research data on environmental pollution. For example, the implementing unit can improve the accuracy of preventive measures by referring to literature on air pollution. Furthermore, the implementing unit can also improve the accuracy of preventive measures by referring to literature on water pollution. For example, the implementing unit can improve the accuracy of preventive measures by referring to relevant literature and research data. In this way, the implementing unit improves the accuracy of preventive measures by referring to relevant literature and research data. Some or all of the above processing in the implementing unit may be performed using AI, for example, or without using AI. For example, the implementing unit can improve the accuracy of preventive measures by analyzing relevant literature and research data using generative AI. In this way, the accuracy of preventive measures improves by referring to relevant literature and research data.

[0057] The monitoring unit can apply algorithms to detect anomalies by referring to past monitoring data during monitoring. For example, the monitoring unit can detect anomalies by referring to past air pollution monitoring data. For example, the monitoring unit can detect anomalies by referring to past water pollution monitoring data. Furthermore, the monitoring unit can also detect anomalies by referring to past environmental monitoring data. For example, the monitoring unit can detect anomalies early by applying algorithms to detect anomalies by referring to past monitoring data. In this way, the monitoring unit can detect anomalies early by referring to past monitoring data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can analyze past monitoring data using generative AI and detect anomalies. In this way, anomalies can be detected early by referring to past monitoring data.

[0058] The monitoring unit can perform comprehensive monitoring by combining different monitoring devices during monitoring. For example, the monitoring unit can perform comprehensive monitoring by combining an air pollution monitoring device and a water pollution monitoring device. For example, the monitoring unit can perform comprehensive monitoring by combining an environmental monitoring device and a health monitoring device. Furthermore, the monitoring unit can perform comprehensive monitoring by combining different monitoring devices. For example, the monitoring unit can perform comprehensive monitoring by combining different monitoring devices. This makes comprehensive monitoring possible by combining different monitoring devices. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can analyze data from different monitoring devices using generative AI and perform comprehensive monitoring. This makes comprehensive monitoring possible by combining different monitoring devices.

[0059] The monitoring unit can determine monitoring priorities by considering geographical location information during monitoring. For example, the monitoring unit can prioritize monitoring areas with high air pollution. For example, the monitoring unit can prioritize monitoring areas where water pollution is occurring. Furthermore, the monitoring unit can also prioritize monitoring areas where environmental events are occurring. For example, the monitoring unit can determine monitoring priorities by considering geographical location information. This allows the monitoring unit to determine priorities by considering geographical location information, enabling efficient monitoring. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can analyze geographical location information using generative AI to determine monitoring priorities. This allows the monitoring unit to determine priorities by considering geographical location information, enabling efficient monitoring.

[0060] The monitoring unit can improve the accuracy of its monitoring by referring to relevant literature and research data during monitoring. For example, the monitoring unit can improve the accuracy of its monitoring by referring to the latest research data on environmental pollution. For example, the monitoring unit can improve the accuracy of its monitoring by referring to literature on air pollution. Furthermore, the monitoring unit can also improve the accuracy of its monitoring by referring to literature on water pollution. For example, the monitoring unit can improve the accuracy of its monitoring by referring to relevant literature and research data. In this way, the monitoring unit improves the accuracy of its monitoring by referring to relevant literature and research data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can improve the accuracy of its monitoring by analyzing relevant literature and research data using generative AI. In this way, the accuracy of its monitoring improves by referring to relevant literature and research data.

[0061] The education delivery unit can select the optimal educational program by referring to past learning data when providing education. For example, the education delivery unit can select the optimal educational program by referring to past learning data. For example, the education delivery unit can select the optimal educational program by referring to past learning outcomes. Furthermore, the education delivery unit can also select the optimal educational program by referring to past learning history. For example, the education delivery unit can select the optimal educational program by referring to past learning data. In this way, the education delivery unit can select the optimal educational program by referring to past learning data. Some or all of the above processing in the education delivery unit may be performed using AI, for example, or without using AI. For example, the education delivery unit can analyze past learning data using generative AI and select the optimal educational program. In this way, the optimal educational program can be selected by referring to past learning data.

[0062] The education delivery department can provide comprehensive education by combining different educational methods. For example, the education delivery department can provide comprehensive education by combining online education and offline education. For example, the education delivery department can provide comprehensive education by combining video education and text education. Furthermore, the education delivery department can also provide comprehensive education by combining different educational methods. For example, the education delivery department can provide comprehensive education by combining different educational methods. This makes comprehensive education possible by combining different educational methods. Some or all of the above-described processes in the education delivery department may be performed using AI, for example, or without AI. For example, the education delivery department can use generative AI to analyze different educational methods and provide comprehensive education. This makes comprehensive education possible by combining different educational methods.

[0063] The education delivery department can select the most suitable educational program by considering geographical location information when providing education. For example, the education delivery department can prioritize providing educational programs in areas with serious environmental problems. For example, the education delivery department can prioritize providing educational programs in areas where environmental events are occurring. Furthermore, the education delivery department can also select the most suitable educational program by considering geographical location information. For example, the education delivery department can select the most suitable educational program by considering geographical location information. This allows the education delivery department to select the most suitable educational program for each region by considering geographical location information. Some or all of the above processing in the education delivery department may be performed using AI, for example, or without using AI. For example, the education delivery department can analyze geographical location information using generative AI and select the most suitable educational program. This allows the education delivery department to select the most suitable educational program for each region by considering geographical location information.

[0064] The education delivery department can improve the accuracy of its educational programs by referring to relevant literature and research data when providing education. For example, the education delivery department can improve the accuracy of its educational programs by referring to the latest research data on environmental education. For example, the education delivery department can improve the accuracy of its educational programs by referring to literature on environmental issues. Furthermore, the education delivery department can also improve the accuracy of its educational programs by referring to literature on environmental protection. For example, the education delivery department can improve the accuracy of its educational programs by referring to relevant literature and research data. In this way, the education delivery department improves the accuracy of its educational programs by referring to relevant literature and research data. Some or all of the above processing in the education delivery department may be performed using AI, for example, or not using AI. For example, the education delivery department can improve the accuracy of its educational programs by analyzing relevant literature and research data using generative AI. In this way, the accuracy of its educational programs improves by referring to relevant literature and research data.

[0065] The reward provision unit can select the optimal reward by referring to past reward data when providing rewards. For example, the reward provision unit can select the optimal reward by referring to past reward data. For example, the reward provision unit can select the optimal reward by referring to past reward history. Furthermore, the reward provision unit can also select the optimal reward by referring to past reward results. For example, the reward provision unit can select the optimal reward by referring to past reward data. In this way, the reward provision unit can select the optimal reward by referring to past reward data. Some or all of the above processing in the reward provision unit may be performed using AI, for example, or without using AI. For example, the reward provision unit can analyze past reward data using generative AI and select the optimal reward. In this way, the optimal reward can be selected by referring to past reward data.

[0066] The reward provision unit can provide comprehensive rewards by combining different reward methods when providing rewards. For example, the reward provision unit can provide comprehensive rewards by combining monetary and non-monetary rewards. For example, the reward provision unit can provide comprehensive rewards by combining a point system and the provision of goods. Furthermore, the reward provision unit can also provide comprehensive rewards by combining different reward methods. For example, the reward provision unit can provide comprehensive rewards by combining different reward methods. This makes it possible for the reward provision unit to provide comprehensive rewards by combining different reward methods. Some or all of the above processing in the reward provision unit may be performed using AI, for example, or without using AI. For example, the reward provision unit can use generative AI to analyze different reward methods and provide comprehensive rewards. This makes it possible for the reward provision unit to provide comprehensive rewards by combining different reward methods.

[0067] The reward provision unit can select the optimal reward by considering geographical location information when providing rewards. For example, the reward provision unit can prioritize providing rewards to areas with serious environmental problems. For example, the reward provision unit can prioritize providing rewards to areas where environmental events are occurring. Furthermore, the reward provision unit can also select the optimal reward by considering geographical location information. For example, the reward provision unit can select the optimal reward by considering geographical location information. This allows the reward provision unit to select the optimal reward for each region by considering geographical location information. Some or all of the above processing in the reward provision unit may be performed using AI, for example, or without using AI. For example, the reward provision unit can analyze geographical location information using generative AI and select the optimal reward. This allows the reward provision unit to select the optimal reward for each region by considering geographical location information.

[0068] The reward provision unit can improve the accuracy of rewards by referring to relevant literature and research data when providing rewards. For example, the reward provision unit can improve the accuracy of rewards by referring to the latest research data on environmental protection. For example, the reward provision unit can improve the accuracy of rewards by referring to literature on environmental issues. Furthermore, the reward provision unit can also improve the accuracy of rewards by referring to literature on environmental protection. For example, the reward provision unit can improve the accuracy of rewards by referring to relevant literature and research data. As a result, the accuracy of rewards is improved by the reward provision unit referring to relevant literature and research data. Some or all of the above processing in the reward provision unit may be performed using AI, for example, or not using AI. For example, the reward provision unit can analyze relevant literature and research data using generative AI to improve the accuracy of rewards. As a result, the accuracy of rewards is improved by referring to relevant literature and research data.

[0069] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0070] The environmental monitoring system can also include a sensor integration unit that combines different sensor devices to collect data from multiple perspectives. For example, when collecting air pollution data, multiple sensors can be used to collect detailed data. Similarly, when collecting water pollution data, different sensors can be combined to collect data. Furthermore, when collecting environmental data, multiple sensors can be used to collect data from multiple perspectives. This means that the sensor integration unit can collect data from multiple perspectives by combining different sensor devices.

[0071] The environmental monitoring system may also include an anomaly detection unit that applies an algorithm to detect abnormal values ​​by comparing them with past data. For example, it can detect abnormal values ​​by comparing them with past air pollution data. It can also detect abnormal values ​​by comparing them with past water pollution data. Furthermore, it can detect abnormal values ​​by comparing them with past environmental data. As a result, the anomaly detection unit can detect abnormal environmental changes at an early stage by detecting abnormal values ​​by comparing them with past data.

[0072] Environmental monitoring systems can also include a data integration unit that integrates different data sources for comprehensive analysis. For example, it can integrate air pollution data and water pollution data for comprehensive analysis. It can also integrate environmental data and social media data for comprehensive analysis. Furthermore, it can integrate data from different sensor devices for comprehensive analysis. As a result, the data integration unit enables comprehensive analysis by integrating different data sources.

[0073] The environmental monitoring system may also include a risk prediction unit that predicts current risks by referring to past risk data. For example, it can predict current risks by referring to past air pollution risk data. It can also predict current risks by referring to past water pollution risk data. Furthermore, it can predict current risks by referring to past environmental risk data. As a result, the risk prediction unit can accurately predict current risks by referring to past risk data.

[0074] The environmental monitoring system may also include a preventive measure selection unit that selects the optimal preventive measure by referring to past implementation data. For example, it can select the optimal preventive measure by referring to past air pollution prevention data. It can also select the optimal preventive measure by referring to past water pollution prevention data. Furthermore, it can select the optimal preventive measure by referring to past environmental prevention data. In this way, the preventive measure selection unit can select the optimal preventive measure by referring to past implementation data.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The collection unit collects environmental data. This environmental data includes temperature, humidity, air quality, and soil data. The collection unit uses IoT devices and sensor devices to collect environmental data such as air pollution and water pollution in real time. For example, it collects temperature data using a temperature sensor, humidity data using a humidity sensor, and air quality data using an air quality sensor. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and generative AI. For example, statistical analysis is used to analyze trends in environmental data, machine learning algorithms are used to analyze environmental data, and generative AI is used to analyze environmental data using natural language processing and image recognition technologies. Step 3: The judgment unit determines the risk of environmental pollution based on the analysis results obtained by the analysis unit. Risk assessment is performed based on risk thresholds and evaluation criteria. For example, a risk threshold is set, and if that threshold is exceeded, the risk is determined to be high. Step 4: The implementation unit implements early warnings and preventive measures based on the risks determined by the decision-making unit. Early warnings and preventive measures are implemented based on the type of warning and the specific means of the preventive measures. For example, early warnings can be issued by raising alerts, and the frequency of collecting environmental data in a specific area can be increased.

[0077] (Example of form 2) The environmental monitoring system according to an embodiment of the present invention is a system that comprehensively supports environmental protection and education by utilizing AI and IoT. This system provides three main functions: real-time collection and analysis of environmental data, support for wildlife conservation activities, and promotion of environmental education and conservation activities. Real-time collection and analysis of environmental data involves collecting environmental data such as air pollution and water pollution in real time using IoT devices. This enables early detection and prevention of environmental pollution. The collected data is analyzed by AI to determine the risk of environmental pollution. For example, the AI ​​processes and analyzes data collected from sensor devices in real time. Furthermore, it utilizes generative AI technologies such as natural language processing and image recognition to extract complex data patterns and trends and evaluate the state of the environment. This enables early warnings and the implementation of preventive measures, minimizing the damage caused by environmental pollution. Next is support for wildlife conservation activities. It supports conservation activities by tracking and monitoring the movements of wild animals using surveillance cameras and drones. For example, video and image data collected from sensor devices such as surveillance cameras and drones are analyzed using generative AI to track the movements and behavioral patterns of wild animals. By utilizing generative AI for pattern recognition and anomaly detection, we can identify key indicators and trends in environmental protection activities and respond immediately. This enables the protection of wildlife habitats and the prevention of illegal hunting. By using AI to analyze animal behavior, we can improve the efficiency of conservation activities. This enhances the effectiveness of conservation efforts and strengthens wildlife protection. Furthermore, we promote environmental education and conservation activities. We use AI to provide environmental education programs and raise environmental awareness. For example, we use generative AI to analyze participants' interests and learning styles and provide environmental education programs tailored to each individual. By tracking users' learning processes and results and providing individualized feedback and improvement measures, we achieve effective environmental education. This helps many people understand the importance of environmental protection and participate in environmental protection activities. We offer incentives and rewards to encourage participation in environmental protection activities. This increases participation in environmental protection activities and improves the effectiveness of environmental protection.This allows the environmental monitoring system to comprehensively collect and analyze environmental data, assess risks, implement preventive measures, support wildlife conservation activities, and provide environmental education.

[0078] The environmental monitoring system according to this embodiment comprises a collection unit, an analysis unit, a decision unit, and an implementation unit. The collection unit collects environmental data. Environmental data includes, but is not limited to, temperature, humidity, air quality, and soil data. The collection unit collects environmental data such as air pollution and water pollution in real time, for example, using IoT devices. The collection unit can also collect environmental data using sensor devices. For example, the collection unit can collect temperature data using a temperature sensor. It can also collect humidity data using a humidity sensor. It can also collect air quality data using an air quality sensor. The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit can analyze trends in environmental data using statistical analysis. It can also analyze environmental data using machine learning algorithms. Furthermore, the analysis unit can also analyze environmental data using generative AI. For example, the generative AI analyzes environmental data using natural language processing or image recognition technology. The decision unit determines the risk of environmental pollution based on the analysis results obtained by the analysis unit. Risk assessment is performed based on, for example, risk thresholds or evaluation criteria. For example, the assessment unit sets risk thresholds and determines that the risk is high if those thresholds are exceeded. Risk can also be assessed based on evaluation criteria. The implementation unit implements early warnings and preventive measures based on the risk assessed by the assessment unit. Early warnings and preventive measures are implemented based on, for example, the type of warning or the specific means of the preventive measure. For example, the implementation unit provides an early warning by issuing an alarm. As a preventive measure, the frequency of collecting environmental data in a specific area can also be increased. This allows the environmental monitoring system to perform environmental data collection, analysis, risk assessment, and preventive measure implementation in a continuous flow. Some or all of the above processes in the collection unit, analysis unit, assessment unit, and implementation unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input data collected from sensor devices into a generating AI, which can then analyze the data.The analysis unit can analyze environmental data using generative AI and make risk assessments. The decision-making unit makes risk assessments using generative AI, and the implementation unit can implement early warnings and preventive measures using generative AI.

[0079] The data collection unit collects environmental data. This environmental data includes, but is not limited to, temperature, humidity, air quality, and soil data. For example, the data collection unit uses IoT devices to collect environmental data such as air and water pollution in real time. Specifically, IoT devices incorporate various sensors that continuously monitor the surrounding environmental data. For instance, a temperature sensor periodically measures the ambient temperature and transmits the data to the data collection unit. A humidity sensor measures the humidity in the air and similarly transmits the data. An air quality sensor detects airborne pollutants such as PM2.5 and CO2 concentration and collects the data. A soil sensor measures soil moisture and nutrient content and provides data. These sensors can transmit data to a central database using wireless communication technology, enabling real-time collection of environmental data. Furthermore, the data collection unit can also mount sensors on mobile devices such as drones and autonomous vehicles to efficiently collect environmental data over a wide area. This allows the data collection unit to collect a wide range of environmental data in real time using diverse devices and means, forming the foundation of an environmental monitoring system.

[0080] The analysis department analyzes the data collected by the collection department. Analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. Specifically, statistical analysis is used to analyze trends in environmental data and detect outliers and patterns. For example, time-series analysis of temperature data can be performed to detect abnormal temperature increases or decreases. When analyzing environmental data using machine learning algorithms, past data is used to build models that predict future environmental changes. For example, temperature, humidity, and air quality data can be used to predict the risk of air pollution in a specific area. Furthermore, the analysis department can also analyze environmental data using generative AI. Generative AI analyzes environmental data using natural language processing and image recognition technologies. For example, generative AI analyzes image data acquired from sensors to detect specific pollution sources or abnormal environmental changes. Generative AI can also automatically generate reports and documents on environmental data using natural language processing technology. This allows the analysis department to analyze the collected data from multiple perspectives and accurately understand the current state of the environment and future risks.

[0081] The decision unit determines the risk of environmental pollution based on the analysis results obtained by the analysis unit. Risk assessment is performed based on, for example, risk thresholds and evaluation criteria, but is not limited to these examples. Specifically, the decision unit sets risk thresholds and determines that the risk is high if those thresholds are exceeded. For example, in air quality data, if the PM2.5 concentration exceeds a certain threshold, it determines that the risk of air pollution is high. Risk can also be determined based on evaluation criteria. For example, temperature, humidity, and air quality data are comprehensively evaluated to make a comprehensive judgment on the environmental risk in a specific area. Furthermore, the decision unit can also perform risk assessment using generative AI. Generative AI learns risk patterns and trends based on past data and statistical information and predicts future risks. For example, generative AI can learn past air pollution data and predict the risk of air pollution under specific weather conditions or seasons. As a result, the decision unit can accurately and quickly determine the risk based on the analysis results and provide information for taking appropriate countermeasures.

[0082] The implementation department implements early warnings and preventive measures based on the risks determined by the judgment department. Early warnings and preventive measures are implemented based on, for example, the type of warning and the specific means of preventive measures, but are not limited to such examples. Specifically, the implementation department provides early warnings by issuing warnings. For example, if the risk of air pollution is determined to be high, the implementation department issues a warning to local residents and notifies them to refrain from going outside. In addition, as a preventive measure, the frequency of collecting environmental data in a particular area can be increased. For example, if the risk of water pollution is determined to be high, the implementation department increases the frequency of collecting water quality data in that area and collects more detailed data. Furthermore, the implementation department can also implement early warnings and preventive measures using generative AI. Generative AI proposes optimal warnings and preventive measures based on data that is updated in real time. For example, generative AI can analyze weather data and environmental data and propose optimal evacuation routes and evacuation locations when the risk in a particular area increases. This allows the implementation department to take swift and appropriate measures based on the determined risks and minimize environmental risks.

[0083] The environmental monitoring system includes a monitoring unit that tracks and monitors the movements of wild animals using surveillance cameras and drones. The monitoring unit, for example, monitors the movements of wild animals using surveillance cameras. Surveillance cameras, for example, can monitor the movements of wild animals in detail using high-resolution cameras. The monitoring unit can also track the movements of wild animals using drones. Drones, for example, can monitor the movements of wild animals while flying over a wide area. Furthermore, the monitoring unit can analyze the monitoring data using generative AI. For example, the monitoring unit inputs video and image data collected from surveillance cameras and drones into the generative AI, which then analyzes the data. This allows the monitoring unit to accurately track the movements and behavioral patterns of wild animals. Some or all of the above-described processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input data collected from surveillance cameras and drones into the generative AI, which then analyzes the data. This allows for monitoring the movements of wild animals and supporting conservation efforts.

[0084] The environmental monitoring system includes an education delivery unit that provides environmental education programs using AI. The education delivery unit, for example, uses generative AI to analyze participants' interests and learning styles and provides individually tailored environmental education programs. For instance, the education delivery unit can analyze participants' learning data using generative AI and provide individually optimized education programs. Furthermore, the education delivery unit can track users' learning processes and outcomes and provide individualized feedback and improvement measures. For example, the education delivery unit can analyze users' learning data using generative AI and provide individualized feedback. This enables the education delivery unit to achieve effective environmental education. Some or all of the above-described processes in the education delivery unit may be performed using AI, for example, or without AI. For example, the education delivery unit can analyze participants' learning data using generative AI and provide individually optimized education programs. This enables the provision of environmental education programs and improves environmental awareness.

[0085] The environmental monitoring system includes a rewards provision unit that offers incentives and rewards to encourage participation in environmental protection activities. The rewards provision unit can, for example, encourage participation in environmental protection activities using a point system. For instance, the unit can award points for participation in environmental protection activities, which can then be exchanged for incentives and rewards. Furthermore, the rewards provision unit can also provide monetary rewards. For example, the unit can offer monetary rewards for participation in environmental protection activities. This allows the rewards provision unit to encourage many people to participate in environmental protection activities. Some or all of the above-described processes in the rewards provision unit may be performed using, for example, AI, or not. For example, the rewards provision unit can use generative AI to analyze participant activity data and provide optimal incentives and rewards. This can promote participation in environmental protection activities and improve the effectiveness of environmental protection.

[0086] The monitoring unit can analyze video and image data using generative AI to track the movements and behavioral patterns of wild animals. For example, the monitoring unit can analyze video and image data collected from surveillance cameras and drones using generative AI. For example, the monitoring unit can analyze and track the movements and behavioral patterns of wild animals using generative AI. Furthermore, the monitoring unit can also perform anomaly detection using generative AI. For example, the monitoring unit can detect abnormal behavioral patterns using generative AI and respond immediately. This allows the monitoring unit to accurately track the movements and behavioral patterns of wild animals. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can analyze data collected from surveillance cameras and drones using generative AI to track the movements and behavioral patterns of wild animals. This allows for accurate tracking of the movements and behavioral patterns of wild animals by using generative AI.

[0087] The monitoring unit can utilize generative AI for pattern recognition and anomaly detection to grasp important indicators and trends in environmental protection activities and respond immediately. For example, the monitoring unit can analyze monitoring data using generative AI to grasp important indicators and trends. For example, the monitoring unit can detect abnormal behavioral patterns using generative AI and respond immediately. Furthermore, the monitoring unit can also grasp important indicators in environmental protection activities using generative AI. For example, the monitoring unit can analyze important indicators in environmental protection activities using generative AI and respond immediately. This allows the monitoring unit to quickly grasp and respond to important indicators and trends in environmental protection activities. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can analyze monitoring data using generative AI to grasp important indicators and trends. This allows the monitoring unit to quickly grasp and respond to important indicators and trends in environmental protection activities by using generative AI.

[0088] The education department can use generative AI to analyze participants' interests and learning styles and provide individually tailored environmental education programs. For example, the education department can use generative AI to analyze participants' learning data and provide individually optimized educational programs. Furthermore, the education department can use generative AI to analyze participants' learning data and provide individualized feedback. This allows the education department to provide individually tailored environmental education programs. Some or all of the above processing in the education department may be performed using AI, or not. For example, the education department can use generative AI to analyze participants' learning data and provide individually optimized educational programs. This allows the education department to provide individually tailored environmental education programs by using generative AI.

[0089] The education delivery department can track users' learning processes and outcomes and provide individualized feedback and improvement measures. For example, the education delivery department can analyze users' learning data using generative AI and provide individualized feedback. Furthermore, the education delivery department can track users' learning processes using generative AI and provide individualized improvement measures. This enables the education delivery department to achieve effective environmental education. Some or all of the above-described processes in the education delivery department may be performed using AI, for example, or without AI. For example, the education delivery department can analyze users' learning data using generative AI and provide individualized feedback and improvement measures. This enables effective environmental education by tracking users' learning processes and outcomes and providing individualized feedback and improvement measures.

[0090] The data collection unit can estimate the user's emotions and adjust the timing of environmental data collection based on the estimated emotions. For example, the data collection unit can estimate the user's emotions and adjust the collection frequency. For example, if the user is stressed, the data collection unit can reduce the collection frequency to lessen the user's burden. Furthermore, if the user is relaxed, the data collection unit can increase the collection frequency to collect more detailed data. For example, if the user is in a hurry, the data collection unit can adjust the collection timing to collect data quickly. In this way, the data collection unit can adjust the timing of environmental data collection according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can estimate the user's emotions using generative AI and adjust the collection timing. In this way, by adjusting the timing of environmental data collection according to the user's emotions, the user's burden can be reduced and efficient data collection can be achieved.

[0091] The data collection unit can change its collection method based on specific environmental conditions or events when collecting environmental data. For example, if air pollution is high, the data collection unit can increase the collection frequency to collect more detailed data. For example, if water pollution occurs, the data collection unit can use specific sensors to collect data. Furthermore, the data collection unit can also change its collection method to collect data when an environmental event occurs. For example, the data collection unit can change its collection method based on specific environmental conditions or events to collect more detailed data. This allows the data collection unit to collect more detailed and relevant data by changing its collection method based on specific environmental conditions or events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use generative AI to analyze environmental conditions or events and change its collection method. This allows the data collection to collect more detailed and relevant data by changing the collection method based on specific environmental conditions or events.

[0092] The data collection unit can collect environmental data from multiple perspectives by combining different sensor devices. For example, when collecting air pollution data, the data collection unit can use multiple sensors to collect detailed data. For example, when collecting water pollution data, the data collection unit can use different sensors to collect data. Furthermore, when collecting environmental data, the data collection unit can use multiple sensors to collect data from multiple perspectives. For example, the data collection unit can collect data from multiple perspectives by combining different sensor devices. This enables the data collection unit to collect data from multiple perspectives by combining different sensor devices. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use generative AI to analyze data from different sensor devices and collect data from multiple perspectives. This enables the data collection from multiple perspectives by combining different sensor devices.

[0093] The data collection unit can estimate the user's emotions and determine the priority of environmental data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting important data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. For example, the data collection unit can determine the priority of environmental data to collect based on the user's emotions and prioritize collecting important data. In this way, the data collection unit can prioritize the collection of important data by determining the priority of environmental data to collect according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can estimate the user's emotions using generative AI and determine the priority of environmental data to collect. In this way, the data collection unit can prioritize the collection of important data by determining the priority of environmental data to collect according to the user's emotions.

[0094] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information when collecting environmental data. For example, the data collection unit can prioritize the collection of data from areas with high air pollution. For example, the data collection unit can prioritize the collection of data from areas where water pollution is occurring. Furthermore, the data collection unit can also prioritize the collection of data from areas where environmental events are occurring. For example, the data collection unit can prioritize the collection of highly relevant data by considering geographical location information. In this way, the data collection unit can prioritize the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can analyze geographical location information using generative AI and prioritize the collection of highly relevant data. In this way, the data collection unit can prioritize the collection of highly relevant data by considering geographical location information.

[0095] The data collection unit can analyze social media activity and collect relevant data when collecting environmental data. For example, the data collection unit can collect data related to environmental issues that are trending on social media. For example, the data collection unit can collect data related to environmental events reported on social media. Furthermore, the data collection unit can also collect environmental data shared on social media. For example, the data collection unit can analyze social media activity and collect relevant data. In this way, the data collection unit can collect relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze social media activity using generative AI and collect relevant data. In this way, relevant data can be collected by analyzing social media activity.

[0096] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can use a simple analysis method. For example, if the user is relaxed, the analysis unit can use a detailed analysis method. Furthermore, if the user is in a hurry, the analysis unit can also use a rapid analysis method. For example, the analysis unit can adjust the data analysis method based on the user's emotions and provide analysis results that are appropriate for the user. In this way, the analysis unit can provide analysis results that are appropriate for the user by adjusting the data analysis method according to the user's emotions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can estimate the user's emotions using generative AI and adjust the data analysis method. In this way, it can provide analysis results that are appropriate for the user by adjusting the data analysis method according to the user's emotions.

[0097] The analysis unit can apply algorithms to detect anomalies by comparing them with past data during analysis. For example, the analysis unit can detect anomalies by comparing them with past air pollution data. For example, the analysis unit can detect anomalies by comparing them with past water pollution data. Furthermore, the analysis unit can also detect anomalies by comparing them with past environmental data. For example, the analysis unit can apply algorithms to detect anomalies by comparing them with past data to detect abnormal environmental changes at an early stage. In this way, the analysis unit can detect abnormal environmental changes at an early stage by detecting anomalies by comparing them with past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can use generative AI to compare with past data and detect anomalies. In this way, abnormal environmental changes can be detected at an early stage by detecting anomalies by comparing them with past data.

[0098] The analysis unit can integrate different data sources to perform a comprehensive analysis during the analysis process. For example, the analysis unit can integrate air pollution data and water pollution data for a comprehensive analysis. For example, the analysis unit can integrate environmental data and social media data for a comprehensive analysis. Furthermore, the analysis unit can integrate data from different sensor devices for a comprehensive analysis. For example, the analysis unit can integrate different data sources for a comprehensive analysis. This enables the analysis unit to perform a comprehensive analysis by integrating different data sources. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to integrate different data sources and perform a comprehensive analysis. This enables a comprehensive analysis by integrating different data sources.

[0099] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. For example, the analysis unit can adjust the display method of the analysis results based on the user's emotions to provide a display that is appropriate for the user. In this way, the analysis unit can provide a display that is appropriate for the user by adjusting the display method of the analysis results according to the user's emotions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can estimate the user's emotions using generative AI and adjust the display method of the analysis results. In this way, a display that is appropriate for the user can be provided by adjusting the display method of the analysis results according to the user's emotions.

[0100] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. For example, the analysis unit may prioritize the analysis of data collected during a specific period. Furthermore, the analysis unit may prioritize the analysis of data from the time an environmental event occurred. For example, the analysis unit can determine the priority of analysis based on the data collection timing and prioritize the analysis of the most recent data. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may use generative AI to analyze the data collection timing and determine the priority of analysis. This allows the analysis to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing.

[0101] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and research data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research data on environmental pollution. For example, the analysis unit can improve the accuracy of its analysis by referring to literature on air pollution. Furthermore, the analysis unit can also improve the accuracy of its analysis by referring to literature on water pollution. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature and research data. In this way, the analysis unit improves the accuracy of its analysis by referring to relevant literature and research data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can improve the accuracy of its analysis by analyzing relevant literature and research data using generative AI. In this way, the accuracy of its analysis improves by referring to relevant literature and research data.

[0102] The decision-making unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated emotions. For example, if the user is stressed, the decision-making unit can relax the risk assessment criteria. For example, if the user is relaxed, the decision-making unit can tighten the risk assessment criteria. Furthermore, if the user is in a hurry, the decision-making unit can make a risk assessment quickly. For example, the decision-making unit can adjust the risk assessment criteria based on the user's emotions and make a risk assessment appropriate for the user. In this way, the decision-making unit can make a risk assessment appropriate for the user by adjusting the risk assessment criteria according to the user's emotions. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can estimate the user's emotions using generative AI and adjust the risk assessment criteria. In this way, it can make a risk assessment appropriate for the user by adjusting the risk assessment criteria according to the user's emotions.

[0103] The decision-making unit can predict the current risk by referring to past risk data when making a decision. For example, the decision-making unit can predict the current risk by referring to past air pollution risk data. For example, the decision-making unit can predict the current risk by referring to past water pollution risk data. Furthermore, the decision-making unit can also predict the current risk by referring to past environmental risk data. For example, the decision-making unit can predict the current risk by referring to past risk data. In this way, the decision-making unit can accurately predict the current risk by referring to past risk data. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can analyze past risk data using generative AI and predict the current risk. In this way, the decision-making unit can accurately predict the current risk by referring to past risk data.

[0104] The decision-making unit can perform a comprehensive risk assessment by combining different risk factors at the time of decision-making. For example, the decision-making unit can perform a comprehensive risk assessment by combining air pollution risk and water pollution risk. For example, the decision-making unit can perform a comprehensive risk assessment by combining environmental risk and health risk. Furthermore, the decision-making unit can also perform a comprehensive risk assessment by combining different risk factors. For example, the decision-making unit can perform a comprehensive risk assessment by combining different risk factors. This makes it possible for the decision-making unit to perform a comprehensive risk assessment by combining different risk factors. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can analyze different risk factors using generative AI and perform a comprehensive risk assessment. This makes it possible for the decision-making unit to perform a comprehensive risk assessment by combining different risk factors.

[0105] The decision-making unit can estimate the user's emotions and determine the priority of risk assessments based on the estimated user emotions. For example, if the user is stressed, the decision-making unit will prioritize important risks. For example, if the user is relaxed, the decision-making unit can prioritize detailed risks. Furthermore, if the user is in a hurry, the decision-making unit can also prioritize risks that can be judged quickly. For example, the decision-making unit can determine the priority of risk assessments based on the user's emotions and prioritize important risks. In this way, the decision-making unit can prioritize important risks by determining the priority of risk assessments according to the user's emotions. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can estimate the user's emotions using generative AI and determine the priority of risk assessments. In this way, important risks can be prioritized by determining the priority of risk assessments according to the user's emotions.

[0106] The decision-making unit can perform risk assessments while considering geographical location information. For example, the decision-making unit can prioritize the assessment of risks in areas with high air pollution. For example, the decision-making unit can prioritize the assessment of risks in areas where water pollution is occurring. Furthermore, the decision-making unit can also prioritize the assessment of risks in areas where environmental events are occurring. For example, the decision-making unit can perform risk assessments while considering geographical location information. This enables the decision-making unit to perform risk assessments for each region by considering geographical location information. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can analyze geographical location information using generative AI and perform risk assessments. This enables risk assessments for each region by considering geographical location information.

[0107] The decision-making unit can assess risk by referring to relevant market data when making a decision. For example, the decision-making unit can assess risk by referring to market data on environmental pollution. For example, the decision-making unit can assess risk by referring to market data on air pollution. Furthermore, the decision-making unit can also assess risk by referring to market data on water pollution. For example, the decision-making unit can assess risk by referring to relevant market data. This improves the accuracy of risk assessment by referring to relevant market data. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can analyze relevant market data using generative AI and assess risk. This improves the accuracy of risk assessment by referring to relevant market data.

[0108] The implementation unit can estimate the user's emotions and adjust the implementation method of preventive measures based on the estimated user emotions. For example, if the user is stressed, the implementation unit can implement simple preventive measures. For example, if the user is relaxed, the implementation unit can implement detailed preventive measures. Furthermore, if the user is in a hurry, the implementation unit can implement preventive measures that can be implemented quickly. For example, the implementation unit can adjust the implementation method of preventive measures based on the user's emotions and implement preventive measures that are appropriate for the user. In this way, the implementation unit can implement preventive measures that are appropriate for the user by adjusting the implementation method according to the user's emotions. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without using AI. For example, the implementation unit can estimate the user's emotions using generative AI and adjust the implementation method of preventive measures. In this way, the implementation unit can implement preventive measures that are appropriate for the user by adjusting the implementation method according to the user's emotions.

[0109] The implementation unit can select the optimal preventive measures by referring to past implementation data during implementation. For example, the implementation unit can select the optimal preventive measures by referring to past air pollution prevention data. For example, the implementation unit can select the optimal preventive measures by referring to past water pollution prevention data. Furthermore, the implementation unit can also select the optimal preventive measures by referring to past environmental prevention data. For example, the implementation unit can select the optimal preventive measures by referring to past implementation data. In this way, the implementation unit can select the optimal preventive measures by referring to past implementation data. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without using AI. For example, the implementation unit can analyze past implementation data using generative AI and select the optimal preventive measures. In this way, the optimal preventive measures can be selected by referring to past implementation data.

[0110] The implementation unit can combine different preventive measures to implement comprehensive measures during implementation. For example, the implementation unit can combine air pollution prevention measures and water pollution prevention measures to implement comprehensive measures. For example, the implementation unit can combine environmental prevention measures and health prevention measures to implement comprehensive measures. Furthermore, the implementation unit can also combine different preventive measures to implement comprehensive measures. For example, the implementation unit can combine different preventive measures to implement comprehensive measures. This makes it possible for the implementation unit to implement comprehensive measures by combining different preventive measures. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can use generative AI to analyze different preventive measures and implement comprehensive measures. This makes it possible to implement comprehensive measures by combining different preventive measures.

[0111] The implementation unit can estimate the user's emotions and determine the priority of preventive measures based on the estimated user emotions. For example, if the user is stressed, the implementation unit can prioritize important preventive measures. For example, if the user is relaxed, the implementation unit can prioritize detailed preventive measures. Furthermore, if the user is in a hurry, the implementation unit can also prioritize preventive measures that can be implemented quickly. For example, the implementation unit can determine the priority of preventive measures based on the user's emotions and prioritize the implementation of important preventive measures. In this way, the implementation unit can prioritize the implementation of important preventive measures by determining the priority of preventive measures according to the user's emotions. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can estimate the user's emotions and determine the priority of preventive measures using generative AI. In this way, important preventive measures can be prioritized by determining the priority of preventive measures according to the user's emotions.

[0112] The implementation unit can select the most appropriate preventive measures at the time of implementation, taking geographical location information into consideration. For example, the implementation unit can prioritize the implementation of preventive measures in areas with high air pollution. For example, the implementation unit can prioritize the implementation of preventive measures in areas where water pollution is occurring. Furthermore, the implementation unit can also prioritize the implementation of preventive measures in areas where environmental events are occurring. For example, the implementation unit can select the most appropriate preventive measures by taking geographical location information into consideration. This allows the implementation unit to select the most appropriate preventive measures for each region by taking geographical location information into consideration. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without using AI. For example, the implementation unit can analyze geographical location information using generative AI and select the most appropriate preventive measures. This allows the implementation unit to select the most appropriate preventive measures for each region by taking geographical location information into consideration.

[0113] The implementing unit can improve the accuracy of preventive measures by referring to relevant literature and research data during implementation. For example, the implementing unit can improve the accuracy of preventive measures by referring to the latest research data on environmental pollution. For example, the implementing unit can improve the accuracy of preventive measures by referring to literature on air pollution. Furthermore, the implementing unit can also improve the accuracy of preventive measures by referring to literature on water pollution. For example, the implementing unit can improve the accuracy of preventive measures by referring to relevant literature and research data. In this way, the implementing unit improves the accuracy of preventive measures by referring to relevant literature and research data. Some or all of the above processing in the implementing unit may be performed using AI, for example, or without using AI. For example, the implementing unit can improve the accuracy of preventive measures by analyzing relevant literature and research data using generative AI. In this way, the accuracy of preventive measures improves by referring to relevant literature and research data.

[0114] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated emotions. For example, if the user is stressed, the monitoring unit can use a simple monitoring method. For example, if the user is relaxed, the monitoring unit can use a detailed monitoring method. Furthermore, if the user is in a hurry, the monitoring unit can also use a rapid monitoring method. For example, the monitoring unit can adjust the monitoring method based on the user's emotions to provide monitoring that is appropriate for the user. This enables the monitoring unit to provide monitoring that is appropriate for the user by adjusting the monitoring method according to the user's emotions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can estimate the user's emotions using generative AI and adjust the monitoring method. This enables monitoring that is appropriate for the user by adjusting the monitoring method according to the user's emotions.

[0115] The monitoring unit can apply algorithms to detect anomalies by referring to past monitoring data during monitoring. For example, the monitoring unit can detect anomalies by referring to past air pollution monitoring data. For example, the monitoring unit can detect anomalies by referring to past water pollution monitoring data. Furthermore, the monitoring unit can also detect anomalies by referring to past environmental monitoring data. For example, the monitoring unit can detect anomalies early by applying algorithms to detect anomalies by referring to past monitoring data. In this way, the monitoring unit can detect anomalies early by referring to past monitoring data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can analyze past monitoring data using generative AI and detect anomalies. In this way, anomalies can be detected early by referring to past monitoring data.

[0116] The monitoring unit can perform comprehensive monitoring by combining different monitoring devices during monitoring. For example, the monitoring unit can perform comprehensive monitoring by combining an air pollution monitoring device and a water pollution monitoring device. For example, the monitoring unit can perform comprehensive monitoring by combining an environmental monitoring device and a health monitoring device. Furthermore, the monitoring unit can perform comprehensive monitoring by combining different monitoring devices. For example, the monitoring unit can perform comprehensive monitoring by combining different monitoring devices. This makes comprehensive monitoring possible by combining different monitoring devices. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can analyze data from different monitoring devices using generative AI and perform comprehensive monitoring. This makes comprehensive monitoring possible by combining different monitoring devices.

[0117] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can provide a simple and highly visible display method. For example, if the user is relaxed, the monitoring unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a concise display method. For example, the monitoring unit can adjust the display method of the monitoring results based on the user's emotions to provide a display that is appropriate for the user. In this way, the monitoring unit can provide a display that is appropriate for the user by adjusting the display method of the monitoring results according to the user's emotions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can estimate the user's emotions using generative AI and adjust the display method of the monitoring results. In this way, a display that is appropriate for the user can be provided by adjusting the display method of the monitoring results according to the user's emotions.

[0118] The monitoring unit can determine monitoring priorities by considering geographical location information during monitoring. For example, the monitoring unit can prioritize monitoring areas with high air pollution. For example, the monitoring unit can prioritize monitoring areas where water pollution is occurring. Furthermore, the monitoring unit can also prioritize monitoring areas where environmental events are occurring. For example, the monitoring unit can determine monitoring priorities by considering geographical location information. This allows the monitoring unit to determine priorities by considering geographical location information, enabling efficient monitoring. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can analyze geographical location information using generative AI to determine monitoring priorities. This allows the monitoring unit to determine priorities by considering geographical location information, enabling efficient monitoring.

[0119] The monitoring unit can improve the accuracy of its monitoring by referring to relevant literature and research data during monitoring. For example, the monitoring unit can improve the accuracy of its monitoring by referring to the latest research data on environmental pollution. For example, the monitoring unit can improve the accuracy of its monitoring by referring to literature on air pollution. Furthermore, the monitoring unit can also improve the accuracy of its monitoring by referring to literature on water pollution. For example, the monitoring unit can improve the accuracy of its monitoring by referring to relevant literature and research data. In this way, the monitoring unit improves the accuracy of its monitoring by referring to relevant literature and research data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can improve the accuracy of its monitoring by analyzing relevant literature and research data using generative AI. In this way, the accuracy of its monitoring improves by referring to relevant literature and research data.

[0120] The education delivery unit can estimate the user's emotions and adjust the content of the educational program based on the estimated emotions. For example, if the user is feeling stressed, the education delivery unit can provide a simple educational program. For example, if the user is relaxed, the education delivery unit can provide a detailed educational program. Furthermore, if the user is in a hurry, the education delivery unit can provide an educational program that allows for rapid learning. For example, the education delivery unit can adjust the content of the educational program based on the user's emotions and provide an educational program that is appropriate for the user. In this way, the education delivery unit can provide an educational program that is appropriate for the user by adjusting the content of the educational program according to the user's emotions. Some or all of the above processing in the education delivery unit may be performed using AI, for example, or without using AI. For example, the education delivery unit can estimate the user's emotions using generative AI and adjust the content of the educational program. In this way, it can provide an educational program that is appropriate for the user by adjusting the content of the educational program according to the user's emotions.

[0121] The education delivery unit can select the optimal educational program by referring to past learning data when providing education. For example, the education delivery unit can select the optimal educational program by referring to past learning data. For example, the education delivery unit can select the optimal educational program by referring to past learning outcomes. Furthermore, the education delivery unit can also select the optimal educational program by referring to past learning history. For example, the education delivery unit can select the optimal educational program by referring to past learning data. In this way, the education delivery unit can select the optimal educational program by referring to past learning data. Some or all of the above processing in the education delivery unit may be performed using AI, for example, or without using AI. For example, the education delivery unit can analyze past learning data using generative AI and select the optimal educational program. In this way, the optimal educational program can be selected by referring to past learning data.

[0122] The education delivery department can provide comprehensive education by combining different educational methods. For example, the education delivery department can provide comprehensive education by combining online education and offline education. For example, the education delivery department can provide comprehensive education by combining video education and text education. Furthermore, the education delivery department can also provide comprehensive education by combining different educational methods. For example, the education delivery department can provide comprehensive education by combining different educational methods. This makes comprehensive education possible by combining different educational methods. Some or all of the above-described processes in the education delivery department may be performed using AI, for example, or without AI. For example, the education delivery department can use generative AI to analyze different educational methods and provide comprehensive education. This makes comprehensive education possible by combining different educational methods.

[0123] The education delivery unit can estimate the user's emotions and prioritize educational programs based on those emotions. For example, if the user is stressed, the education delivery unit can prioritize important educational programs. For example, if the user is relaxed, the education delivery unit can prioritize detailed educational programs. Furthermore, if the user is in a hurry, the education delivery unit can prioritize educational programs that allow for quick learning. For example, the education delivery unit can prioritize educational programs based on the user's emotions and prioritize important educational programs. In this way, the education delivery unit can prioritize important educational programs by determining the priority of educational programs according to the user's emotions. Some or all of the above processing in the education delivery unit may be performed using AI, for example, or without AI. For example, the education delivery unit can estimate the user's emotions using generative AI and determine the priority of educational programs. In this way, by determining the priority of educational programs according to the user's emotions, important educational programs can be prioritized.

[0124] The education delivery department can select the most suitable educational program by considering geographical location information when providing education. For example, the education delivery department can prioritize providing educational programs in areas with serious environmental problems. For example, the education delivery department can prioritize providing educational programs in areas where environmental events are occurring. Furthermore, the education delivery department can also select the most suitable educational program by considering geographical location information. For example, the education delivery department can select the most suitable educational program by considering geographical location information. This allows the education delivery department to select the most suitable educational program for each region by considering geographical location information. Some or all of the above processing in the education delivery department may be performed using AI, for example, or without using AI. For example, the education delivery department can analyze geographical location information using generative AI and select the most suitable educational program. This allows the education delivery department to select the most suitable educational program for each region by considering geographical location information.

[0125] The education delivery department can improve the accuracy of its educational programs by referring to relevant literature and research data when providing education. For example, the education delivery department can improve the accuracy of its educational programs by referring to the latest research data on environmental education. For example, the education delivery department can improve the accuracy of its educational programs by referring to literature on environmental issues. Furthermore, the education delivery department can also improve the accuracy of its educational programs by referring to literature on environmental protection. For example, the education delivery department can improve the accuracy of its educational programs by referring to relevant literature and research data. In this way, the education delivery department improves the accuracy of its educational programs by referring to relevant literature and research data. Some or all of the above processing in the education delivery department may be performed using AI, for example, or not using AI. For example, the education delivery department can improve the accuracy of its educational programs by analyzing relevant literature and research data using generative AI. In this way, the accuracy of its educational programs improves by referring to relevant literature and research data.

[0126] The reward provider can estimate the user's emotions and adjust the reward content based on the estimated emotions. For example, if the user is stressed, the reward provider can provide a simple reward. For example, if the user is relaxed, the reward provider can provide a detailed reward. Furthermore, if the user is in a hurry, the reward provider can provide a reward that can be received quickly. For example, the reward provider can adjust the reward content based on the user's emotions and provide a reward that is appropriate for the user. In this way, the reward provider can provide a reward that is appropriate for the user by adjusting the reward content according to the user's emotions. Some or all of the above processing in the reward provider may be performed using AI, for example, or without using AI. For example, the reward provider can estimate the user's emotions using generative AI and adjust the reward content. In this way, the reward provider can provide a reward that is appropriate for the user by adjusting the reward content according to the user's emotions.

[0127] The reward provision unit can select the optimal reward by referring to past reward data when providing rewards. For example, the reward provision unit can select the optimal reward by referring to past reward data. For example, the reward provision unit can select the optimal reward by referring to past reward history. Furthermore, the reward provision unit can also select the optimal reward by referring to past reward results. For example, the reward provision unit can select the optimal reward by referring to past reward data. In this way, the reward provision unit can select the optimal reward by referring to past reward data. Some or all of the above processing in the reward provision unit may be performed using AI, for example, or without using AI. For example, the reward provision unit can analyze past reward data using generative AI and select the optimal reward. In this way, the optimal reward can be selected by referring to past reward data.

[0128] The reward provision unit can provide comprehensive rewards by combining different reward methods when providing rewards. For example, the reward provision unit can provide comprehensive rewards by combining monetary and non-monetary rewards. For example, the reward provision unit can provide comprehensive rewards by combining a point system and the provision of goods. Furthermore, the reward provision unit can also provide comprehensive rewards by combining different reward methods. For example, the reward provision unit can provide comprehensive rewards by combining different reward methods. This makes it possible for the reward provision unit to provide comprehensive rewards by combining different reward methods. Some or all of the above processing in the reward provision unit may be performed using AI, for example, or without using AI. For example, the reward provision unit can use generative AI to analyze different reward methods and provide comprehensive rewards. This makes it possible for the reward provision unit to provide comprehensive rewards by combining different reward methods.

[0129] The reward system can estimate the user's emotions and prioritize rewards based on those emotions. For example, if the user is stressed, the reward system may prioritize important rewards. For example, if the user is relaxed, the reward system may prioritize detailed rewards. Furthermore, if the user is in a hurry, the reward system may prioritize rewards that can be received quickly. For example, the reward system can prioritize rewards based on the user's emotions and prioritize important rewards. This allows the reward system to prioritize important rewards by determining the priority of rewards according to the user's emotions. Some or all of the above processing in the reward system may be performed using AI, for example, or not using AI. For example, the reward system can use generative AI to estimate the user's emotions and determine the priority of rewards. This allows the reward system to prioritize important rewards by determining the priority of rewards according to the user's emotions.

[0130] The reward provision unit can select the optimal reward by considering geographical location information when providing rewards. For example, the reward provision unit can prioritize providing rewards to areas with serious environmental problems. For example, the reward provision unit can prioritize providing rewards to areas where environmental events are occurring. Furthermore, the reward provision unit can also select the optimal reward by considering geographical location information. For example, the reward provision unit can select the optimal reward by considering geographical location information. This allows the reward provision unit to select the optimal reward for each region by considering geographical location information. Some or all of the above processing in the reward provision unit may be performed using AI, for example, or without using AI. For example, the reward provision unit can analyze geographical location information using generative AI and select the optimal reward. This allows the reward provision unit to select the optimal reward for each region by considering geographical location information.

[0131] The reward provision unit can improve the accuracy of rewards by referring to relevant literature and research data when providing rewards. For example, the reward provision unit can improve the accuracy of rewards by referring to the latest research data on environmental protection. For example, the reward provision unit can improve the accuracy of rewards by referring to literature on environmental issues. Furthermore, the reward provision unit can also improve the accuracy of rewards by referring to literature on environmental protection. For example, the reward provision unit can improve the accuracy of rewards by referring to relevant literature and research data. As a result, the accuracy of rewards is improved by the reward provision unit referring to relevant literature and research data. Some or all of the above processing in the reward provision unit may be performed using AI, for example, or not using AI. For example, the reward provision unit can analyze relevant literature and research data using generative AI to improve the accuracy of rewards. As a result, the accuracy of rewards is improved by referring to relevant literature and research data.

[0132] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0133] The environmental monitoring system may also include an emotion adjustment unit that estimates the user's emotions and adjusts the method of collecting environmental data based on the estimated emotions. For example, if the user is stressed, the emotion adjustment unit can reduce the collection frequency to alleviate the user's burden. Conversely, if the user is relaxed, the emotion adjustment unit can increase the collection frequency to collect more detailed data. Also, if the user is in a hurry, the emotion adjustment unit can adjust the collection timing to collect data quickly. In this way, the emotion adjustment unit can flexibly adjust the method of collecting environmental data according to the user's emotions.

[0134] The environmental monitoring system can also include a sensor integration unit that combines different sensor devices to collect data from multiple perspectives. For example, when collecting air pollution data, multiple sensors can be used to collect detailed data. Similarly, when collecting water pollution data, different sensors can be combined to collect data. Furthermore, when collecting environmental data, multiple sensors can be used to collect data from multiple perspectives. This means that the sensor integration unit can collect data from multiple perspectives by combining different sensor devices.

[0135] The environmental monitoring system may also include an emotion analysis unit that estimates the user's emotions and adjusts the data analysis method based on the estimated emotions. For example, if the user is stressed, the emotion analysis unit can use a simple analysis method. Conversely, if the user is relaxed, the emotion analysis unit can use a detailed analysis method. Also, if the user is in a hurry, the emotion analysis unit can use a rapid analysis method. This allows the emotion analysis unit to flexibly adjust the data analysis method according to the user's emotions.

[0136] The environmental monitoring system may also include an anomaly detection unit that applies an algorithm to detect abnormal values ​​by comparing them with past data. For example, it can detect abnormal values ​​by comparing them with past air pollution data. It can also detect abnormal values ​​by comparing them with past water pollution data. Furthermore, it can detect abnormal values ​​by comparing them with past environmental data. As a result, the anomaly detection unit can detect abnormal environmental changes at an early stage by detecting abnormal values ​​by comparing them with past data.

[0137] The environmental monitoring system may also include an emotion display unit that estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the emotion display unit can provide a simple and highly visible display method. Conversely, if the user is relaxed, the emotion display unit can provide a display method that includes detailed information. Also, if the user is in a hurry, the emotion display unit can provide a display method that gets straight to the point. In this way, the emotion display unit can flexibly adjust the display method of the analysis results according to the user's emotions.

[0138] Environmental monitoring systems can also include a data integration unit that integrates different data sources for comprehensive analysis. For example, it can integrate air pollution data and water pollution data for comprehensive analysis. It can also integrate environmental data and social media data for comprehensive analysis. Furthermore, it can integrate data from different sensor devices for comprehensive analysis. As a result, the data integration unit enables comprehensive analysis by integrating different data sources.

[0139] The environmental monitoring system may also include an emotion judgment unit that estimates the user's emotions and adjusts the risk assessment criteria based on those emotions. For example, if the user is stressed, the emotion judgment unit can relax the risk assessment criteria. Conversely, if the user is relaxed, the emotion judgment unit can tighten the risk assessment criteria. Also, if the user is in a hurry, the emotion judgment unit can make a risk assessment quickly. In this way, the emotion judgment unit can flexibly adjust the risk assessment criteria according to the user's emotions.

[0140] The environmental monitoring system may also include a risk prediction unit that predicts current risks by referring to past risk data. For example, it can predict current risks by referring to past air pollution risk data. It can also predict current risks by referring to past water pollution risk data. Furthermore, it can predict current risks by referring to past environmental risk data. As a result, the risk prediction unit can accurately predict current risks by referring to past risk data.

[0141] The environmental monitoring system may also include an emotion implementation unit that estimates the user's emotions and adjusts the implementation of preventive measures based on those emotions. For example, if the user is stressed, the emotion implementation unit can implement simple preventive measures. Conversely, if the user is relaxed, the emotion implementation unit can implement more detailed preventive measures. Also, if the user is in a hurry, the emotion implementation unit can implement preventive measures that can be taken quickly. This allows the emotion implementation unit to flexibly adjust the implementation of preventive measures according to the user's emotions.

[0142] The environmental monitoring system may also include a preventive measure selection unit that selects the optimal preventive measure by referring to past implementation data. For example, it can select the optimal preventive measure by referring to past air pollution prevention data. It can also select the optimal preventive measure by referring to past water pollution prevention data. Furthermore, it can select the optimal preventive measure by referring to past environmental prevention data. In this way, the preventive measure selection unit can select the optimal preventive measure by referring to past implementation data.

[0143] The following briefly describes the processing flow for example form 2.

[0144] Step 1: The collection unit collects environmental data. This environmental data includes temperature, humidity, air quality, and soil data. The collection unit uses IoT devices and sensor devices to collect environmental data such as air pollution and water pollution in real time. For example, it collects temperature data using a temperature sensor, humidity data using a humidity sensor, and air quality data using an air quality sensor. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and generative AI. For example, statistical analysis is used to analyze trends in environmental data, machine learning algorithms are used to analyze environmental data, and generative AI is used to analyze environmental data using natural language processing and image recognition technologies. Step 3: The judgment unit determines the risk of environmental pollution based on the analysis results obtained by the analysis unit. Risk assessment is performed based on risk thresholds and evaluation criteria. For example, a risk threshold is set, and if that threshold is exceeded, the risk is determined to be high. Step 4: The implementation unit implements early warnings and preventive measures based on the risks determined by the decision-making unit. Early warnings and preventive measures are implemented based on the type of warning and the specific means of the preventive measures. For example, early warnings can be issued by raising alerts, and the frequency of collecting environmental data in a specific area can be increased.

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0147] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, decision unit, and implementation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects environmental data using the sensor device of the smart device 14 and analyzes the collected data by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The decision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines the risk of environmental pollution based on the analysis results. The implementation unit is implemented by, for example, the control unit 46A of the smart device 14 and implements early warnings and preventive measures based on the determined risk. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0150] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, decision unit, and implementation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects environmental data using the sensor device of the smart glasses 214 and analyzes the collected data using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The decision unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and determines the risk of environmental pollution based on the analysis results. The implementation unit is implemented, for example, in the control unit 46A of the smart glasses 214 and implements early warnings and preventive measures based on the determined risk. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0166] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the collection unit, analysis unit, decision unit, and implementation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects environmental data using the sensor device of the headset terminal 314 and analyzes the collected data by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The decision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines the risk of environmental pollution based on the analysis results. The implementation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and implements early warnings and preventive measures based on the determined risk. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0181] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0182] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0183] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0184] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0185] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0186] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0187] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0188] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0189] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0190] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0191] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0192] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0193] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0194] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0195] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0196] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0197] Each of the multiple elements described above, including the collection unit, analysis unit, decision unit, and implementation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects environmental data using the sensor devices of the robot 414 and analyzes the collected data by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The decision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines the risk of environmental pollution based on the analysis results. The implementation unit is implemented by, for example, the control unit 46A of the robot 414 and implements early warnings and preventive measures based on the determined risk. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0198] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0199] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0200] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0201] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0202] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0203] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0204] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0205] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0206] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0207] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0208] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0209] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0210] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0211] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0212] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0213] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0214] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0215] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0216] (Note 1) A collection unit that collects environmental data, An analysis unit analyzes the data collected by the aforementioned collection unit, A judgment unit that determines the risk of environmental pollution based on the analysis results obtained by the aforementioned analysis unit, The system includes an implementation unit that implements early warnings and preventive measures based on the risks determined by the aforementioned determination unit. A system characterized by the following features. (Note 2) The facility includes a monitoring unit that uses surveillance cameras and drones to track and monitor the movements of wild animals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The facility includes an education department that provides environmental education programs utilizing AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a rewards department that provides perks and rewards to encourage participation in environmental protection activities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned monitoring unit, Using generative AI, we analyze video and image data to track the movements and behavioral patterns of wild animals. The system described in Appendix 2, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, By utilizing generative AI for pattern recognition and anomaly detection, we can identify key indicators and trends in environmental protection activities and respond immediately. The system described in Appendix 2, characterized by the features described herein. (Note 7) The aforementioned education provision department, Using generative AI, we analyze participants' interests and learning styles to provide personalized environmental education programs. The system described in Appendix 3, characterized by the features described herein. (Note 8) The aforementioned education provision department, Track users' learning progress and achievements, and provide personalized feedback and improvement suggestions. The system described in Appendix 3, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of environmental data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting environmental data, change the collection method based on specific environmental conditions or events. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting environmental data, combine different sensor devices to collect data from multiple perspectives. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates user sentiment and prioritizes the environmental data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting environmental data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting environmental data, analyze social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, an algorithm is applied to detect outliers by comparing them with historical data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, different data sources are integrated to perform a comprehensive analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, we refer to relevant literature and research data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The unit that makes the determination said, We estimate user sentiment and adjust risk assessment criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The unit that makes the determination said, When making a decision, we refer to past risk data to predict the current risk. The system described in Appendix 1, characterized by the features described herein. (Note 23) The unit that makes the determination said, When making a decision, a comprehensive risk assessment is conducted by combining different risk factors. The system described in Appendix 1, characterized by the features described herein. (Note 24) The unit that makes the determination said, It estimates user sentiment and determines the priority of risk assessments based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The unit that makes the determination said, When making a decision, risk assessment should take geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The unit that makes the determination said, When making a decision, we refer to relevant market data to assess the risks. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned implementation unit is We estimate the user's emotions and adjust the implementation of preventative measures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned implementation unit is During implementation, the optimal preventive measures will be selected by referring to past implementation data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned implementation unit is When implementing measures, a comprehensive approach will be taken by combining different preventive measures. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned implementation unit is The system estimates user sentiment and prioritizes preventative measures based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned implementation unit is When implementing the measures, the most appropriate preventive measures will be selected, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned implementation unit is During implementation, refer to relevant literature and research data to improve the accuracy of preventive measures. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned monitoring unit, The system estimates user sentiment and adjusts monitoring methods based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned monitoring unit, During monitoring, an algorithm is applied that detects anomalies by referring to past monitoring data. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned monitoring unit, During monitoring, a combination of different monitoring devices is used for comprehensive monitoring. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned monitoring unit, When monitoring, geographical location information is taken into consideration to determine monitoring priorities. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned monitoring unit, During monitoring, we improve the accuracy of monitoring by referring to relevant literature and research data. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned education provision department, The system estimates the user's emotions and adjusts the content of the educational program based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned education provision department, When providing education, the optimal educational program is selected by referring to past learning data. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned education provision department, When providing education, we combine different educational methods to provide comprehensive education. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned education provision department, It estimates user emotions and prioritizes educational programs based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned education provision department, When providing education, the most suitable educational program is selected, taking geographical location information into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned education provision department, When providing education, we refer to relevant literature and research data to improve the accuracy of the educational program. The system described in Appendix 3, characterized by the features described herein. (Note 45) The aforementioned compensation provision unit, The system estimates the user's emotions and adjusts the reward content based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned compensation provision unit, When providing rewards, the system selects the optimal reward by referring to past reward data. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned compensation provision unit, When providing compensation, a comprehensive compensation system is implemented by combining different compensation methods. The system described in Appendix 4, characterized by the features described herein. (Note 48) The aforementioned compensation provision unit, The system estimates the user's emotions and prioritizes rewards based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 49) The aforementioned compensation provision unit, When providing rewards, the system selects the most appropriate reward based on geographical location information. The system described in Appendix 4, characterized by the features described herein. (Note 50) The aforementioned compensation provision unit, When providing rewards, refer to relevant literature and research data to improve the accuracy of the rewards The system according to appended note 4, characterized by this

Explanation of symbols

[0217] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects environmental data, An analysis unit analyzes the data collected by the aforementioned collection unit, A judgment unit that determines the risk of environmental pollution based on the analysis results obtained by the aforementioned analysis unit, The system includes an implementation unit that implements early warnings and preventive measures based on the risks determined by the aforementioned determination unit. A system characterized by the following features.

2. The facility includes a monitoring unit that uses surveillance cameras and drones to track and monitor the movements of wild animals. The system according to feature 1.

3. It has an education department that provides environmental education programs using AI. The system according to feature 1.

4. It includes a rewards department that provides perks and rewards to encourage participation in environmental protection activities. The system according to feature 1.

5. The aforementioned monitoring unit, Using generative AI, we analyze video and image data to track the movements and behavioral patterns of wild animals. The system according to feature 2.

6. The aforementioned monitoring unit, By utilizing generative AI for pattern recognition and anomaly detection, we can identify key indicators and trends in environmental protection activities and respond immediately. The system according to feature 2.

7. The aforementioned education provision department, Using generative AI, we analyze participants' interests and learning styles to provide personalized environmental education programs. The system according to claim 3.

8. The aforementioned education provision department, Track users' learning progress and achievements, and provide personalized feedback and improvement suggestions. The system according to claim 3.

9. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of environmental data collection based on the estimated user emotions. The system according to feature 1.

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